def test__sumAllWeights(self):
smoothingMetadata = dc.Test_DefaultSmoothingOscilationWeightedMean_Metadata(
test_smoothingEndCheckType='wgsum')
smoothing = dc.DefaultSmoothingOscilationWeightedMean(
smoothingMetadata=smoothingMetadata)
smoothing.__setDictionary__(
smoothingMetadata=smoothingMetadata,
dictionary=self.model.getNNModelModule().named_parameters())
smoothing.countWeights = 1
smoothing.calcMean(model=self.model,
smoothingMetadata=smoothingMetadata)
sumWg = smoothing._sumAllWeights(smoothingMetadata=smoothingMetadata,
metadata=self.metadata)
ut.testCmpPandas(sumWg, 'weight_sum', 58.0)
def test_statistics(self):
self.timer.start()
self.timer.end()
self.timer.timeStart = 1.0
self.timer.timeEnd = 2.5
self.timer.addToStatistics()
self.timer.timeStart = 3.0
self.timer.timeEnd = 5.5
self.timer.addToStatistics()
self.timer.timeStart = 10.0
self.timer.timeEnd = 25.7
self.timer.addToStatistics()
ut.testCmpPandas(self.timer.getTimeSum(), "timer_sum", (2.5 - 1.0) + (5.5 - 3.0) + (25.7 - 10.0))
ut.testCmpPandas(self.timer.getAverage(), "timer_sum", ((2.5 - 1.0) + (5.5 - 3.0) + (25.7 - 10.0)) / 3)
def test__smoothingGoodEnoughCheck(self):
smoothingMetadata = dc.Test_DefaultSmoothingOscilationWeightedMean_Metadata(
test_softMarginAdditionalLoops=0,
test_weightsEpsilon=1.0,
test_smoothingEndCheckType='wgsum')
smoothing = dc.DefaultSmoothingOscilationWeightedMean(
smoothingMetadata=smoothingMetadata)
smoothing.__setDictionary__(
smoothingMetadata=smoothingMetadata,
dictionary=self.model.getNNModelModule().named_parameters())
smoothing.countWeights = 1
a = 2.0
b = 1.5
ut.testCmpPandas(
smoothing._smoothingGoodEnoughCheck(
abs(a - b), smoothingMetadata=smoothingMetadata), 'bool',
False)
ut.testCmpPandas(
smoothing._smoothingGoodEnoughCheck(
abs(a - b), smoothingMetadata=smoothingMetadata), 'bool', True)
b = 3.1
ut.testCmpPandas(
smoothing._smoothingGoodEnoughCheck(
abs(a - b), smoothingMetadata=smoothingMetadata), 'bool',
False)
def checkOscilation__isSmoothingGoodEnough__(self, avgLoss, helperEpoch,
avgKLoss, dataMetadata,
smoothing, smoothingMetadata,
helper, model, metadata,
booleanIsGood):
smoothing(helperEpoch=helperEpoch,
helper=helper,
model=model,
dataMetadata=dataMetadata,
modelMetadata=None,
metadata=metadata,
smoothingMetadata=smoothingMetadata)
ut.testCmpPandas(smoothing.lossContainer.getAverage(), 'average',
avgLoss)
ut.testCmpPandas(
smoothing.lossContainer.getAverage(
smoothingMetadata.lossContainerDelayedStartAt), 'average',
avgKLoss)
ut.testCmpPandas(
smoothing.__isSmoothingGoodEnough__(
helperEpoch=helperEpoch,
helper=helper,
model=model,
dataMetadata=None,
modelMetadata=None,
metadata=metadata,
smoothingMetadata=smoothingMetadata), 'isSmoothingGoodEnough',
booleanIsGood)
def test_pinMemory(self):
ok = False
if(torch.cuda.is_available()):
ok = True
metadata = sf.Metadata(debugInfo=False)
model_metadata = sf.Model_Metadata()
data_metadata = sf.Data_Metadata()
ut.testCmpPandas(data_metadata.pin_memoryTrain, "pin_memory_train", False)
ut.testCmpPandas(data_metadata.pin_memoryTest, "pin_memory_test", False)
data_metadata.tryPinMemoryTrain(metadata, model_metadata)
ut.testCmpPandas(data_metadata.pin_memoryTrain, "pin_memory_train", ok)
data_metadata.tryPinMemoryTest(metadata, model_metadata)
ut.testCmpPandas(data_metadata.pin_memoryTest, "pin_memory_test", ok)
def test_imprint_2(self):
state = sf.LoopsState()
ok = state.decide()
ut.testCmpPandas(ok, "loopState_loop_here", 0)
state.imprint(64, True)
state.imprint(32, True)
tmp = pickle.dumps(state)
state = pickle.loads(tmp)
ok = state.decide()
ut.testCmpPandas(ok, "loopState_go_next", None)
ok = state.decide()
ut.testCmpPandas(ok, "loopState_go_next", None)
ok = state.decide()
ut.testCmpPandas(ok, "loopState_loop_here", 0)
def test_iteration(self):
inst = sf.CircularList(3)
inst.pushBack(1)
inst.pushBack(2)
inst.pushBack(3)
i = iter(inst)
ut.testCmpPandas(i.indexArray, 'array', [2, 1, 0])
ut.testCmpPandas(next(i), 'iter_next', 3)
ut.testCmpPandas(next(i), 'iter_next', 2)
ut.testCmpPandas(next(i), 'iter_next', 1)
self.assertRaises(StopIteration, lambda : next(i))
inst.pushBack(4)
i = iter(inst)
ut.testCmpPandas(next(i), 'iter_next', 4)
ut.testCmpPandas(next(i), 'iter_next', 3)
ut.testCmpPandas(next(i), 'iter_next', 2)
self.assertRaises(StopIteration, lambda : next(i))
inst.pushBack(5)
i = iter(inst)
ut.testCmpPandas(next(i), 'iter_next', 5)
ut.testCmpPandas(next(i), 'iter_next', 4)
ut.testCmpPandas(next(i), 'iter_next', 3)
self.assertRaises(StopIteration, lambda : next(i))
def test_getAverage(self):
inst = sf.CircularList(3)
ut.testCmpPandas(inst.getAverage(), 'average', 0)
inst.pushBack(1)
ut.testCmpPandas(inst.getAverage(), 'average', 1.0)
inst.pushBack(2)
ut.testCmpPandas(inst.getAverage(), 'average', 1.5)
inst.pushBack(3)
ut.testCmpPandas(inst.getAverage(), 'average', 2.0)
ut.testCmpPandas(inst.getAverage(startAt=1), 'average', 1.5)
inst.pushBack(4)
ut.testCmpPandas(inst.getAverage(), 'average', 3.0)
inst.pushBack(5)
ut.testCmpPandas(inst.getAverage(), 'average', 4.0)
ut.testCmpPandas(inst.getAverage(startAt=1), 'average', 3.5)
ut.testCmpPandas(inst.getAverage(startAt=2), 'average', 3.0)
inst.reset()
ut.testCmpPandas(len(inst.array), 'array_length', 0)
inst.pushBack(10)
ut.testCmpPandas(inst.getAverage(), 'average', 10.0)
ut.testCmpPandas(len(inst.array), 'array_length', 1)
ut.testCmpPandas(inst.getAverage(startAt=1), 'average', 0)
def test_pushBack(self):
inst = sf.CircularList(2)
inst.pushBack(1)
ut.testCmpPandas(inst.array[0], 'array_value', 1)
inst.pushBack(2)
ut.testCmpPandas(inst.array[0], 'array_value', 1)
ut.testCmpPandas(inst.array[1], 'array_value', 2)
inst.pushBack(3)
ut.testCmpPandas(inst.array[0], 'array_value', 3)
ut.testCmpPandas(inst.array[1], 'array_value', 2)
inst.pushBack(4)
ut.testCmpPandas(inst.array[0], 'array_value', 3)
ut.testCmpPandas(inst.array[1], 'array_value', 4)
inst.reset()
ut.testCmpPandas(len(inst.array), 'array_length', 0)
inst.pushBack(10)
ut.testCmpPandas(inst.array[0], 'array_value', 10)
ut.testCmpPandas(len(inst.array), 'array_length', 1)
def test_enterExit(self):
ut.testCmpPandas(sf.test_mode.isActive(), "test_mode_plain", False)
with sf.test_mode():
ut.testCmpPandas(sf.test_mode.isActive(), "test_mode_on", True)
ut.testCmpPandas(sf.test_mode.isActive(), "test_mode_turn_off", False)
def test_onOff(self):
ut.testCmpPandas(sf.test_mode.isActive(), "test_mode_plain", False)
sf.StaticData.TEST_MODE = True
ut.testCmpPandas(sf.test_mode.isActive(), "test_mode_on", True)
sf.StaticData.TEST_MODE = False
ut.testCmpPandas(sf.test_mode.isActive(), "test_mode_turn_off", False)
def test_sequence_2(self):
sampler = sf.BaseSampler(dataSize=10, batchSize=2, startIndex=2, seed=988)
testList = [4, 5, 6, 7, 8, 9]
random.Random(988).shuffle(testList)
ut.testCmpPandas(sampler.sequence, "sampler_sequence_2", testList)
def test_len(self):
inst = sf.CircularList(3)
ut.testCmpPandas(len(inst), 'array_length', 0)
inst.pushBack(1)
ut.testCmpPandas(len(inst), 'array_length', 1)
inst.pushBack(2)
ut.testCmpPandas(len(inst), 'array_length', 2)
inst.pushBack(3)
ut.testCmpPandas(len(inst), 'array_length', 3)
inst.pushBack(4)
ut.testCmpPandas(len(inst), 'array_length', 3)
inst.pushBack(5)
ut.testCmpPandas(len(inst), 'array_length', 3)